Objective To explore the influencing factors of the usage behavior of the scientific research management system and provide references for hospitals in constructing scientific research management systems. Methods Data were collected through questionnaires in April 2024. Based on the unified theory of acceptance and use of technology (UTAUT), the information system success model, and the self-efficacy theory, a research model on the influencing factors of the usage behavior of the scientific research management system among medical staff was constructed from the dual perspectives of users and information systems. The structural equation model was utilized to explore the influencing factors of the usage behavior of the scientific research management system. Results A total of 527 questionnaires were collected. Among them, there were 157 males and 370 females. The overall Cronbach α coefficient of the questionnaire was 0.916, and the KMO value was 0.896. For Bartlett’s test of sphericity (P<0.001). The composite reliability of each latent variable was greater than 0.7, and the average variance extracted was greater than 0.5. Therefore, this questionnaire had good reliability and validity. The research showed that information quality, performance expectancy, effort expectancy, and system quality all had significant positive impacts on the usage intention of users of the scientific research management system (P<0.05). Meanwhile, facilitating conditions and usage intention both had significant positive impacts on the usage behavior of users (P<0.05). Conclusions The construction of the scientific research management system should be guided by management needs, comprehensively sort out the general scientific research work needs of medical staff. Through the apply information-based means, various forms of training, and strengthening policy guidance, the aim is to improve the intelligence level of system operations, enhance the convenience of user self-service, and promote the effective construction of the ecosystem of the scientific research management system.
Regurgitation is an abnormal condition happens when left ventricular assist devices (LVADs) operated at a low speed, which causes LVAD to fail to assist natural blood-pumping by heart and thus affects patients’ health. According to the degree of regurgitation, three LVAD’s regurgitation states were identified in this paper: no regurgitation, slight regurgitation and severe regurgitation. Regurgitation index (RI), which is presented based on the theory of dynamic closed cavity, is used to grade the regurgitation of LVAD. Numerical results showed that when patients are in exercising, resting and sleeping state, the critical speed between slight regurgitation and no regurgitation are 6 650 r/min, 7 000 r/min and 7 250 r/min, respectively, with corresponding RI of 0.401, 0.300 and 0.238, respectively. And the critical speed between slight regurgitation and severe regurgitation are 5 500 r/min, 6 000 r/min and 6 450 r/min, with corresponding RI of 0.488, 0.359 and 0.284 respectively. In addition, there is a negative relation correction between RI and rotational speed, so that grading the LVAD’s regurgitation can be achieved by determining the corresponding critical speed. Therefore, the detective parameter RI based on the signal of flow is proved to be able to grade LVAD’s regurgitation states effectively and contribute to the detection of LVAD’s regurgitation, which provides theoretical basis and technology support for developing a LVADs controlling system with high reliability.
Currently, about one-third of patients with anti-epilepsy drug or resective surgery continue to have sezure, the mechanism remin unknown. Up to date, the main target for presurgical evaluation is to determene the EZ and SOZ. Since the early nineties of the last century network theory was introduct into neurology, provide new insights into understanding the onset, propagation and termination. Focal seizure can impact the function of whole brain, but the abnormal pattern is differet to generalized seizure. Brain network is a conception of mathematics. According to the epilepsy, network node and hub are related to the treatment. Graphy theory and connectivity are main algorithms. Understanding the mechanism of epilepsy deeply, since study the theory of epilepsy network, can improve the planning of surgery, resection epileptogenesis zone, seizure onset zone and abnormal node of hub simultaneously, increase the effect of resectiv surgery and predict the surgery outcome. Eventually, develop new drugs for correct the abnormal network and increase the effect. Nowadays, there are many algorithms for the brain network. Cooperative study by the clinicans and biophysicists instituted standard and extensively applied algorithms is the precondition of widely used clinically.
Objective To introduce a new theory of shoulder stability mechanism, rebalancing theory, and clinical application of this new theory for the shoulder instability and dysfunction of motion. Methods Through extensive review of the literature related to shoulder instability and dysfunction of the motion in recent years, combined with our clinical practice experience, the internal relation between passive stability mechanism and dynamic stability mechanism were summarized. Results Rebalancing theory of shoulder stability mechanism is addressed, namely, when the shoulder stability mechanism is destructive, the stability of the shoulder can be restored by the rebalance between dynamic stability mechanism and passive stability mechanism. When dynamic stability is out of balance, dynamic stability can be restored by rebalancing the different parts of dynamic stability mechanism or to strengthen the passive stability mechanism. When passive stability mechanism is out of balance, passive stability can be restored by rebalancing the soft tissue and bone of the shoulder. ConclusionRebalancing theory of shoulder stability mechanism could make a understanding the occurrence, development, and prognosis of shoulder instability and dysfunction from a comprehensive and dynamic view and guide the treatment effectively.
With the range of application of computational biology and systems biology gradually expanding, the complexity of the bioprocess models is also increased. To address this difficult problem, it is required to introduce positive alternative analysis method to cope with it. Taking the dynamic model of the epidemic control process as research object, we established an evaluation model in our laboratory. Firstly, the model was solved with nonlinear programming method. The results were shown to be good. Based on biochemical systems theory, the ODE dynamic model was transformed into S-system. The eigen values of the model showed that the system was stable and contained oscillation phenomenon. Next the sensitivities of rate constant and logarithmic gains of the three key parameters were analyzed, as well as the robust of the system. The result indicated that the biochemical systems theory could be applied in different fields more widely.
At present, balanced scorecard is widely used in hospital performance management, but because of the difficulty in selecting indicators and the ambiguity of causality, its implementation on the application level is limited. Based on the theory of “competitive advantage” and resource arrangement, this study constructs an improved balanced scorecard index system for discipline performance evaluation from the perspective of improving discipline competitiveness of Ningbo No.2 Hospital. The index system mainly includes the dimensions of discipline quality development, resource allocation efficiency and so on, with the characteristics of “focusing on advantages, accurate positioning” and innovation, and has achieved good results in practical application.
Abstract There have been many types of bone-grafting operation dealing with the congenital pseudarthrosis of the tibia (CPAT), but the failure rate is fairly high. Since 1990, 2 children with CPAT, who had received repeated bone-grafting operation in failure, were treatedaccordingto the Ilizarovs theory and its related technique. The essentials of the operation were: (1) Thorough resection of abnormal bone tissues at pseudarthrosis might freshen the ends of the bone and facilitate bone union. (2) The firm fixation and constant compression force to the bone ends might promote bone healing. (3) The tibial lengthening was performed by osteotomy at the upper tibia. The pseudarthrosis was united in 2～3 months after operation. The patients were followed up from 1 to 3 years, and it was found that the remodelling of thebone was good, no recurrence occured. The advantages were: (1) The bone grafting was no longer necessary. (2) It could give a chance to equalizethe limb length. (3) It could enable early weight bearing and functional exercise.
Developmental and epileptic encephalopathy (DEE) is a group of diseases that severely affects the neurological development of children, characterized by frequent seizures and significant neurodevelopmental impairments. These diseases not only impact the quality of life of affected children but also impose a heavy burden on families and society. In recent years, the development of brain network theory has provided a new perspective on understanding the pathological mechanisms of DEE, especially the role of structural and functional brain networks in the process of epilepsy. This review systematically summarized the research progress of structural and functional brain networks in DEE, highlighted their importance in seizure activity, disease progression, and prognosis evaluation.
With the change of COVID-19, the prevention and control of COVID-19 infection epidemic entered a new stage in December 2022. How to quickly complete the emergency treatment of a large number of patients in a short period of time, and ensure that patients in emergency department can get rapid and effective medical treatment has always been an urgent problem that emergency department need to solve. The Department of Emergency Medicine of West China Hospital of Sichuan University has adopted patient-oriented management measures based on the core idea of the new public management theory, and has achieved remarkable results. Therefore, this article summarizes the workflow and nursing management strategies of the emergency department rescue area of West China Hospital of Sichuan University in dealing with the batch treatment of COVID-19 infected patients, including optimizing and correcting the environment layout of the ward, implementing the “secondary triage” mode in the rescue area, adding an inter-hospital referral platform for critical patients with COVID-19 emergency, building a conventional COVID-19 reserve material repository in the emergency department, setting up a field office for multi-department joint emergency admission service, optimizing emergency transport services for patients with COVID-19, scientific scheduling and reasonable human resource management, and providing humanistic care for employees, in order to provide reference for the management practice of the emergency department.
Traditional sample entropy fails to quantify inherent long-range dependencies among real data. Multiscale sample entropy (MSE) can detect intrinsic correlations in data, but it is usually used in univariate data. To generalize this method for multichannel data, we introduced multivariate multiscale entropy into multiscale signals as a reflection of the nonlinear dynamic correlation. But traditional multivariate multiscale entropy has a large quantity of computation and costs a large period of time and space for more channel system, so that it can not reflect the correlation between variables timely and accurately. In this paper, therefore, an improved multivariate multiscale entropy embeds on all variables at the same time, instead of embedding on a single variable as in the traditional methods, to solve the memory overflow while the number of channels rise, and it is more suitable for the actual multivariate signal analysis. The method was tested in simulation data and Bonn epilepsy dataset. The simulation results showed that the proposed method had a good performance to distinguish correlation data. Bonn epilepsy dataset experiment also showed that the method had a better classification accuracy among the five data set, especially with an accuracy of 100% for data collection of Z and S.
